Abstract
Circular trading is a fraudulent trading scheme used by notorious tax evaders with the motivation to trick the tax enforcement authorities from identifying their suspicious transactions. Dealers make use of this technique to collude with each other and hence do heavy illegitimate trade among themselves to hide suspicious sales transactions. In this paper, we develop an algorithm to detect the group of colluding dealers who do heavy illegitimate trading among themselves. We formulate the problem as finding clusters in a weighted directed graph. Novelty of our approach is that we used Benford’s analysis to define weights and defined a measure similar to F1 score to find similarity between two clusters. The proposed algorithm is run on the commercial tax data set given by the government of Telangana, India, and the results obtained contains a group of several colluding dealers.
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Priya, Mathews, J., Kumar, K.S., Babu, C.S., Rao, S.V.K.V. (2019). A Collusion Set Detection in Value Added Tax Using Benford’s Analysis. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_70
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DOI: https://doi.org/10.1007/978-3-030-01174-1_70
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